def init_setup():
data = Dataset(root='/tmp/', name=args.dataset, setting='gcn')
data.features = normalize_feature(data.features)
adj, features, labels = data.adj, data.features, data.labels
StaticGraph.graph = nx.from_scipy_sparse_matrix(adj)
dict_of_lists = nx.to_dict_of_lists(StaticGraph.graph)
idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test
device = torch.device('cuda') if args.ctx == 'gpu' else 'cpu'
# black box setting
adj, features, labels = preprocess(adj,
features,
labels,
preprocess_adj=False,
sparse=True,
device=device)
victim_model = load_victim_model(data,
device=device,
file_path=args.saved_model)
setattr(victim_model, 'norm_tool',
GraphNormTool(normalize=True, gm='gcn', device=device))
output = victim_model.predict(features, adj)
loss_test = F.nll_loss(output[idx_test], labels[idx_test])
acc_test = accuracy(output[idx_test], labels[idx_test])
print("Test set results:", "loss= {:.4f}".format(loss_test.item()),
"accuracy= {:.4f}".format(acc_test.item()))
return features, labels, idx_val, idx_test, victim_model, dict_of_lists, adj
# data = Dataset(root='/tmp/', name=args.dataset, setting='nettack', seed=15)
# Or we can just use setting='prognn' to get the splits
# data = Dataset(root='./tmp/', name=args.dataset, setting='prognn')
# adj, features, labels_1 = data.adj, data.features, data.labels
# idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test
# load pre-attacked graph
# perturbed_data = PrePtbDataset(root='./tmp/',
# name=args.dataset,
# attack_method='meta',
# ptb_rate=args.ptb_rate)
# use data splist provided by prognn
data = Dataset(root='./tmp/', name=args.dataset, setting='prognn')
data.adj = sp.csr_matrix(adj_per)
data.features = sp.csr_matrix(base_feat)
data.labels = labels
data.idx_train = idx_train
data.idx_val = idx_val
data.idx_test = idx_test
perturbed_adj = sp.csr_matrix(adj_per, dtype=float)
features = sp.csr_matrix(base_feat, dtype=float)
# Setup Defense Model
gat = GAT(nfeat=features.shape[1],
nhid=8,
heads=8,
nclass=labels.max().item() + 1,
dropout=0.5,
device=device)